inclusive razor analysis results – 27-10-11crogan/files/razor_27_10_11.pdf · inclusive razor...

+

Inclusive Razor Analysis Results – 27-10-11

A. Apresyan, Y. Chen, Christopher Rogan, J. Duarte, E. Di Marco, J. Lykken, A. Mott, M. Pierini, W. Reece, E. Salvati, M. Spiropulu

[GeV]RM100 150 200 250 300 350 400

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GeV

evt

N

1

10

210

310

410R > 0.15R > 0.20R > 0.25R > 0.30R > 0.35R > 0.40R > 0.45R > 0.50

=7 TeVsCMS 2010 Preliminary

-1 L dt = 35 pb∫ [GeV] (R* > 0.2)R*M

R*γ

200 400 600 800 1000 1200 1400

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GeV

evt

N

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510 tAll tTCHELTCHEMTCHETTCHEL+TCHELTCHEM+TCHELTCHET+TCHEL

=7 TeVsCMS 2011 Simulation

[GeV] (R* > 0.2)R*MR*γ

200 400 600 800 1000 1200 1400

)A

LL /

Ni C

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(N 00.20.40.60.8

1

+ Analysis Status 2

n  800 pb-1 analysis has been green-lighted for approval (Nov. 9)

n  CADI: http://cms.cern.ch/iCMS/jsp/analysis/admin/analysismanagement.jsp?ancode=SUS-11-008

n  Twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SUS11008

+ The 2011 Razor Analysis

n Based on variables MR and R (as last year)

n Use six exclusive boxes (three last year) defined according to lepton multiplicity

n In each box, define several exclusive signal regions (rather than one per box)

n Obtain a bkg prediction through a ML fit in the low R/MR sideband using a 2D parameterization (last year 1D)

n Combine several boxes/signal regions in one inclusive hypothesis test (for limit on the cross section or for accessing discovery)

3

+ 4 Search with MR + R (Razor) Introduced “Razor” variables, R and MR, designed to discover and characterize massive pair-production

Scale:

= �MMET

MRT =

�| �M |(|�p|+ |�q|)− �M · (�p+ �q)

2

q̃q̃ → (qχ̃01)(qχ̃

01)Example:

M∆ =m2

q̃ −m2χ̃01

2mq̃

M∆ =m2

q̃ −m2χ̃01

2mq̃

Peaks at

Edge at

Angle: R =MR

T

MR

MR =�

(|�p|+ |�q|)2 − (pz + qz)2

arXiv:1006.2727

�p �qArranging all reconstructed objects into two hemispheres, with 3-momenta and

+ 5 Search with MR + R (Razor) Introduced “Razor” variables, R and MR, designed to discover and characterize massive pair-production

Scale:

= �MMET

MRT =

�| �M |(|�p|+ |�q|)− �M · (�p+ �q)

2

Angle: R =MR

T

MR

MR =�

(|�p|+ |�q|)2 − (pz + qz)2

arXiv:1006.2727

�p �qArranging all reconstructed objects into two hemispheres, with 3-momenta and

tt̄+ jetsW + jets

+ Razor Triggers 6 See previous trigger talks:

u  In addition to fully hadronic triggers, there are R/MR x-triggers with: Æ  Single Muon Æ  Single Electron Æ  B-tagged jet Æ  Single Photon Æ  Double Photon

u  Deployed online after May technical stop

https://indico.cern.ch/getFile.py/access?contribId=6&resId=0&materialId=slides&confId=141034 https://indico.cern.ch/getFile.py/access?contribId=6&resId=0&materialId=slides&confId=135391

n  Suite of Razor triggers designed to capture events in most interesting region of R/MR plane

Used in the analysis presented See back-up slides for more details

+ Datasets

n  Boxes indicate PD’s w/ Razor triggers

7

+ Baseline Event selection 8

…

L2L3 Corrected Calo Jets w/ FastJet PU subtraction

L2L3 Corrected PF Jets

L2L3 Corrected PF Jets w/ FastJet PU subtraction

Uncorrected track Jets

pT > 40 GeV/c

pT > 15 GeV/c

|η| < 3.0

|η| < 2.4pT > 40 GeV/cpT > 40 GeV/c

|η| < 3.0 |η| < 3.0

Loose Jet ID Loose Jet ID Loose Jet ID Consistent

w/ PV

and more

n  Standard HCAL DPG HBHE Noise Filter + other event filters (see back-up)

n  Jet ID and selection

n  Require at least 2 jets with pT > 60 GeV/c (requirement from L1 trigger seed) - All jets (of a given type) clustered into two mega-jets

n  two mega-jets and PF MET are used to calculate variables R and MR

+ Baseline Event selection 9

L2L3 Corrected Calo Jets w/ FastJet PU subtraction

pT > 40 GeV/c

|η| < 3.0

Loose Jet ID

n  Standard HCAL DPG HBHE Noise Filter + other event filters (see back-up)

n  Jet ID and selection

n  Require at least 2 jets with pT > 60 GeV/c (requirement from L1 trigger seed) - All jets (of a given type) clustered into two mega-jets

n  two mega-jets and PF MET are used to calculate variables R and MR

Default used in this analysis Chosen for consistency with online trigger objects

+ Offline Lepton Selection 10

Electrons Muons

See: https://twiki.cern.ch/twiki/bin/view/CMS/SimpleCutBasedEleID2011

Cut based selection a la VBTF 2010

We use ‘WP80’ and ‘WP95’

Cut based selection identical to VBTF 2010, with ‘Tight’ and ‘Loose’ working points except for isolation

(see below)

When isolation requirements are applied (WP80,95 and Tight muon) the combined (ECAL+HCAL+tracker) isolation is used, and is corrected for

PU dependence using the FastJet-derived energy density ρ. The PU-corrected combined isolation for isolation cone size R, ISOR

CORR, can be expressed in terms of the non-corrected quantity, ISOR

UNCORR, as:

ISOCORR

R = ISOUNCORR

R − πR2ρ

+MR shape dependence on Lep ID 11

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Loose ID

Tight ID


[GeV] (R* > 0.2)R*MR*γ

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Ni C

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(N 0.80.850.9

0.951

W+jets Madgraph MC CALO JETS

n  We want to understand the potential effect of our muon selection on the distribution of MR

n  Define ‘baseline/denominator’ as all simulated events with a generator level muon within acceptance (pT and η) [ ]

n  Apply lepton ID on top of baseline to see effect on MR distribution

n  Only small dependence observed

All W (µν)

MR [GeV] (R > 0.2)

MR [GeV] (R > 0.2)

+ 12

[GeV] (R* > 0.2)R*MR*γ

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WP 80


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W+jets Madgraph MC CALO JETS

MR shape dependence on Lep ID

n  We want to understand the potential effect of our electron selection on the distribution of MR

n  Define ‘baseline/denominator’ as all simulated events with a generator level electron within acceptance (pT and η) [ ]

n  Apply lepton ID on top of baseline to see effect on MR distribution

n  Only small dependence observed (stronger in ‘turn-on’ low MR region)

All W (eν)

MR [GeV] (R > 0.2)

MR [GeV] (R > 0.2)

+ Final State Boxes 13

MU Box

o  ‘Tight’ muon

HAD Box

o  Veto on lepton boxes

u  Disjoint boxes based on physics object ID allows us to isolate different physics processes

u  Lepton boxes, along with a QCD control sample, are used for the background prediction in the hadronic signal box (along with predictions in lepton boxes’ signal regions)

u  Possibilities for sub-divisions within ‘boxes’ (isolation inversion for QCD, b-tag categories, lepton charge(s), etc.)

pµT > 10 GeV/c

MUMU Box

o  ‘Tight’ muon+ ‘Loose’ muon

EMU Box

o  ‘Tight’ muon+ WP80 electron

EE Box

o  WP80 electron+ WP95 electron

pµT > 10 GeV/c

ELE Box

o  WP80 electron

peT > 20 GeV/c peT > 20/10 GeV/cpµT > 15/10 GeV/c

peT > 20 GeV/c

+ 14

MU Box

HAD Box

MUMU Box

EMU Box

ELE Box

Final State Boxes (Tight MU pT > 10 && WP80 ELE pT > 20)?

(Tight/Tight MU pT > 15/10)?

(WP80/WP95 ELE pT > 20/10)?

EE Box

(Tight MU pT > 10)?

(WP80 ELE pT > 20)?

NO

NO

NO

NO

NO

YES

YES

YES

YES

YES

Box selection hierarchy ensures orthogonality of different box datasets

+ ML Fit Strategy

15

MU Box

o  ‘Tight’ muon

HAD Box

o  Veto on lepton boxes

ELE Box

o  WP80 electron pµT > 10 GeV/c

MUMU Box

o  ‘Tight’ muon+ ‘Loose’ muon EMU Box

o  ‘Tight’ muon+ WP80 electron

EE Box

o  WP80 electron+ WP95 electron

Z+jets top+X

W+jets

Put it all together…

pµT > 10 GeV/c peT > 20 GeV/c

peT > 20/10 GeV/cpµT > 15/10 GeV/c

peT > 20 GeV/c

MORE ON THIS LATER

15

+1D MR Views

n  Pre-scale, low-threshold jet triggers allow us to probe kinematic region dominated by QCD mult-jets

n  MR shape well-modeled by single exponential (in this region)

n  Slope of this exponential has linear dependence on value of (R cut)2

16 Jet PD + (HLT_DiJetAve30 || HLT_Jet30)

f(MR) ∝ e−SMR

S = a+ b(R cut)2

+1D R2 Views

n  Pre-scale, low-threshold jet triggers allow us to probe kinematic region dominated by QCD mult-jets

n  R2 shape well-modeled by single exponential (in this region)

n  Slope of this exponential has linear dependence on value of (R cut)2

17 Jet PD + (HLT_DiJetAve30 || HLT_Jet30)

S = c+ d(MR cut)

f(R2) ∝ e−SR2

+ From 1D To 2D n  1-D projections of MR(R2) and slope dependence on R2(MR) cut implies

specific 2-D functional form:

n  Most general function which recovers the MR (R2) exponential behavior when integrated over R2 (MR)

With:

f(R2,MR) ∝�k(MR −M0

R)(R2 −R2

0)− 1�e−k(MR−M0

R)(R2−R20)

b (from MR view) = d (from R2 view) = k (from 2D view)

18

Confirmed in MC and data

+ MR shape dep. on B-tag 19

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Ni C

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(N 00.20.40.60.8

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tt+jets Madgraph MC CALO JETS

n  We want to understand the potential effect of b-tagging on the distribution of MR

n  We consider the ‘Track Counting High Efficiency’ (TCHE) tagger with the loose (L), medium (M) and tight (T) working points

n  The inclusive ttbar MR distribution (all final state boxes) is compared against single and double b-tag su-samples’

n  Strong dependence observed in low MR ‘turn-on’ region – small shape dependence in exponentially falling region of MR

MR [GeV] (R > 0.2)

MR [GeV] (R > 0.2)

+ 1D MR view (W+jets) 20

PD SingleMu May10 ReReco + HLT_IsoMu17+TCHEM b-tag veto

n  Functionally, same is true for W+jets (in fact, holds for all background considered)

n  Modeled with 2 distinct components (as last year)

n  Each component has unique functional parameters

+ 1D MR view (W+jets) 21


W+jets Madgraph simulation, same offline selection as above

Excellent q

ualitative and q

uantitative d

ata / MC

agreem

ent

+ 1D R2 view (W+jets) 22


W+jets Madgraph simulation, same offline selection as above

Excellent q

ualitative and q

uantitative d

ata / MC

agreem

ent

+ From 1D To 2D n  1-D projections of MR(R2) and slope dependence on R2(MR) cut implies

specific 2-D functional form (unique parameters for each ‘component’):

n  Each relevant background (W+jets, Z+jets, ttbar+jets, …) is described with two unique 2D components:

23

+ Multi-Box Fit Introduction

n Each box contains several contributions n Wln, Zll, TTj, Znunu: Main SM backgrounds

n Each may have one or two components n Second components may be in common (e.g. ISR)

n The characteristic mass scales are different n MW, MZ, MTT

n Different shapes in R2/MR plain (turn on/tails)

n MR and R2 are correlated n Use 2D PDF to take advantage of this

24

+ The Data Control Samples n We consider the May10 events

only (no Razor Trigger)

n We classify events in the leptonic boxes by btag

n We further require mll > 60 GeV to enrich the di-lepton box in Zll events

OneLepton 0btag 1btag

70% ttbar

90% V+jets

SF TwoLeptons 0btag 1btag

80% ttbar

90% V+jets

OF TwoLeptons 0btag 1btag

95% ttbar

50% V+jets

✗

✔ ✔

✔ ✔ ✔

25

Control selections allow us to isolate specific backgrounds Parameters determined from these samples used as constraints in fits to ‘final’ fits on PromptReco (Razor triggered) samples

+ The Pieces Together n  Identify a sideband in the R2 vs MR plane (box-dependent)

n  In each box perform an extended and unbinned Maximum Likelihood Fit

n  Once the shapes are fixed, they are used to predict the background in the signal regions (the tail)

n  The extrapolation is done with toy MC samples, which allow inclusion of the error on parameters

26

R2

MR

Minimum R2 and MR set by trigger requirements

Fit Region

Signal Sensitive Region

+ Results – ELE-ELE Box 27

n  Background predictions in signal sensitive region are propagated analytically from Fit Region

n  Systematic uncertainties are ‘marginalized in’ through toy generations (using full covariance matrix from ML Fit)

p−

value


n  Projections from 2D fits

n  Shows data entire R2/MR plane

n  Here, error bands on ‘SM total’ are statistical only

n  In some cases, background sub-components are ‘effective’ in that they do represent more than one background b/c for example:

We find that all ‘2nd’ (less steep) components are identical within fit precision

+ Results – MU-MU Box 29

p−

value

+ Results – MU-ELE Box 31

p−

value


Background almost exclusively ttbar

+ Results – ELE Box 33

p−

value

+ Results – MU Box 35

p−

value

+ Data/MC Agreement Summary 37

P-value0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

# / b

in (0

.05)

0

1

2

3

4

5

+ Model-Dependent Interpretation 38

n Explicit test of background only hypothesis vs. signal-plus-background hypothesis in signal-sensitive region, model-by-model, using likelihood ratio test-statistic

n Signal represented by 2-D binned templates (with corresponding systematic uncertainties – see next slide)

v.s.


n  Signal systematics are ‘marginalized in’ through systematic variations of shapes and normalizations of signal template in toy generation

n  NOTE: Box-by-box correlations are taken into account in toy generation (relevant for multi-box combined limit)


n  Tag-and-probe DATA/MC correction factors (pT- and η- dependent) are derived from data and used to correct signal yield central values (background is completely data-driven and is insensitive to DATA/MC disagreement)

+ Example Hypothesis Test 42

= log(Q) - All Boxesλ

-100 -50 0 50 100 150 200 250 300

a.u.

0

0.01

0.02

0.03

0.04

0.05

0.06

observedλ

b1-CL

s+bCL

-1 L dt = 800 pb∫CMS Preliminary -

= 240 GeV0M = 500 GeV1/2M = 10βtan

= 00A = +µsgn

= 2.6e-02sCL


-100 -50 0 50 100 150 200 250 300a.

u.0

0.01

0.02

0.03

0.04

0.05

0.06

| b only)λP(

| s+b)λP(λMedian expected

68% prob expected band

-1 L dt = 800 pb∫CMS Preliminary -

= 240 GeV0M = 500 GeV1/2M = 10βtan

= 00A = +µsgn

+ Example Hypothesis Test 43

= log(Q) - MU-MU Boxλ

-200 -100 0 100 200 300 400

a.u.

0

0.02

0.04

0.06

0.08

0.1

0.12

= 1800 GeV0M = 240 GeV1/2M = 10βtan

= 00A = +µsgn

= log(Q) - HAD Boxλ

-200 -100 0 100 200 300 400

a.u.

0

0.01

0.02

0.03

0.04

0.05

0.06 = 1800 GeV0M

= 240 GeV1/2M = 10βtan

= 00A = +µsgn


-200 -100 0 100 200 300 400

a.u.

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

| b only)λP(

| s+b)λP(

= 1800 GeV0M = 240 GeV1/2M = 10βtan

= 00A = +µsgn

+

+

Boxes are combined by adding test-statistic (log-likelihood ratio)

+ … other boxes

+ Final Model-dependent result 44

)2 (GeV/c0m0 500 1000 1500 2000

)2 (G

eV/c

1/2

m

200

400

600

800

(250)GeVq~

(250)GeVg~

(500)GeVq~

(500)GeVg~

(750)GeVq~

(750)GeVg~

(1000)GeVq~

(1000)GeVg~

(1250)GeVq~

(1250)GeVg~

(1500)GeVq~

(1500)GeVg~

-1Razor 800 pb

-1 = 7 TeV, Ldt = 800 pbs ∫CMS Preliminary

> 0µ = 0, 0

= 10, Aβtan

<0µ=5, βtan, q~, g~CDF <0µ=3, βtan, q~, g~D0

±

1χ∼LEP2 ±l~LEP2

= L

SPτ∼

95% C.L. Limits 1LepMedian Expected Limit

σ1±Expected Limit Observed LimitObserved Limit, HadObserved Limit, Lep

-1 = 7 TeV, Ldt = 800 pbs

+ Final Model-dependent result 45

)2 (GeV/c0m0 500 1000 1500 2000

)2 (G

eV/c

1/2

m

200

400

600

800

(250)GeVq~

(250)GeVg~

(500)GeVq~

(500)GeVg~

(750)GeVq~

(750)GeVg~

(1000)GeVq~

(1000)GeVg~

(1250)GeVq~

(1250)GeVg~

(1500)GeVq~

(1500)GeVg~

-1Razor 800 pb

-1 = 7 TeV, Ldt = 800 pbs ∫CMS Preliminary

> 0µ = 0, 0

= 10, Aβtan

<0µ=5, βtan, q~, g~CDF <0µ=3, βtan, q~, g~D0

±

1χ∼LEP2 ±l~LEP2

= L

SPτ∼

95% C.L. Limits 1LepMedian Exp. (Lep)

σ1±Exp. Limit (Lep) Observed Limit, Lep

-1 = 7 TeV, Ldt = 800 pbs

+ 46

BACK-UP SLIDES

+ Event Filters 47

+ Razor Triggers in the Menu

n  Rates are taken from run 166033 and scaled to 1e33

n  Rates for the pure razor triggers match estimates to 2 decimal places

n  x-trigger object thresholds dictate design of final state ‘boxes’

48

+ Razor Single Leptons 49

n  May10ReReco+”Golden” JSON (through 167746)

n  Background shapes appear under control è extending analysis to ML Fit

n  Here, background prediction from MC w/ PU event re-weighting and NLO x-sections

[GeV] (R > 0.5)RM400 600 800 1000 1200 1400

Even

ts / 4

0 G

eV

1

10

210

DATASM MCW+jetstop+XZ+jets

DiBosons


-1 L dt = 726 pb∫

MU Box WP80* PT 10 GeV Tight* PT 20 GeV ELE Box

Isolation Quantities corrected for PU

[GeV] (R > 0.5)RM400 600 800 1000 1200 1400

Even

ts / 4

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eV

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310DATASM MCW+jetsZ+jetstop+X

DiBosons


-1 L dt = 759 pb∫

PD E

lectronHad

+

HLT_E

le10_CaloId

L_TrkIdV

L_CaloIsoV

L _TrkIsoV

L_R025_M

R200_v*

PD M

uHad

+

HLT_M

u8_R025_M

R200_v*

+ Razor Di-Leptons PD

Doub

leElectron +

[GeV] (R > 0.5)RM200 400 600 800 1000

Even

ts / 4

0 G

eV

1

10

210 DATASM MCW+jetstop+XZ+jets

DiBosons


-1 L dt = 708 pb∫

WP80 / Tight*

PT 20 / 10 GeV

e μ ELE-MU Box

[GeV] (R > 0.5)RM100 200 300 400 500 600 700 800

Even

ts / 4

0 G

eV

1

10

210DATASM MCW+jetstop+XZ+jets

DiBosons


-1 L dt = 708 pb∫

HLT_E

le17_CaloId

L_CaloIsoV

L_ E

le8_CaloId

L_CaloIsoV

L_v*

MU

-MU

Box E

LE-E

LE Box

WP80/WP95*

PT 20/10 GeV

[GeV] (R > 0.5)RM200 400 600 800 1000 1200

Even

ts / 4

0 G

eV

1

10

210

DATASM MCW+jetstop+XZ+jets

DiBosons


-1 L dt = 708 pb∫

PT 15/10 GeV Tight/Tight*

Isolation quantities corrected for PU

PD D

oubleM

uon +

HLT_D

oubleM

u7_v* || H

LT_Mu13_M

u8_v*

+ The Second Component

A second component is needed in the kinematic region we did not fully probe last year (statistics) We use an ISR tagger based on MC-truth information:

- look for events with high-pT parton - generated by ME, not PYTHIA parton shower - in these events, the ttbar system recoils against the high-pT parton

The second component in each ttbar final state (hadronic, semileptonic, and dileptonic) is the same as for ISR-tagged events

51

+ Is the 2nd component universal?

n Apply fit to MC in each box n  Cross-check for each MC sample

n Are the 2nd component parameters the same? n We might expect them to be due to ISR n 1st components expected to be different

Zjets MC in lepton boxes Shapes very compatible Fractions different but invariant under number of Btags

Shapes also invariant under number of Btags Can use B-veto to get clean sample of Zll. True for 1st component also


p−

value

p−

value


p−

value

+ Results – HAD Box 59

p−

value


p−

value

+ x-sections 62

Hadro-production x-sections

+ Signal Contamination 63

[GeV]0M500 1000 1500 2000

[GeV

]1/

2M

300

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500

600

700

HA

D B

ox S

igna

l Con

tam

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= 10 AβCMSSM tan

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]1/

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D B

ox S

igna

l Con

tam

. (%

)

-110

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=7 TeVsCMS Simulation = +µ = -500 sgn 0

= 40 AβCMSSM tan

Plots show percent signal contamination in the fit region, calculated w.r.t. the actual number of observed events in data

tanβ = 10 tanβ = 40

Signal contamination in the fit region of the different boxes is small for models that are outside of the 35 pb-1 analysis exclusions – very small

around 750 pb-1 observed limit

HAD BOX HAD BOX


[GeV]0M500 1000 1500 2000

[GeV

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2M

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Box

Sign

al C

onta

m. (

%)

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m. (

%)

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tanβ = 10



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[GeV]0M400 600 800 100012001400160018002000

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nal C

onta

m. (

%)

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0 = 40 AβCMSSM tan



ELE BOX MU BOX tanβ = 40


+ Signal Eff. tan β=10 66

[GeV]0M500 1000 1500 2000

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]1/

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al R

egio

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f. (%

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Eff.

(%)

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0 = 10 AβCMSSM tan

All Signal Regions (all boxes) HAD Box Signal Regions

Efficiency calculated w.r.t. inclusive SUSY cross-section


MU Box Signal Regions


[GeV]0M500 1000 1500 2000

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3.5=7 TeVsCMS Simulation = +µ = 0 sgn

0 = 10 AβCMSSM tan

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4.5=7 TeVsCMS Simulation = +µ = 0 sgn

0 = 10 AβCMSSM tan

ELE Box Signal Regions


All Signal Regions (all boxes) HAD Box Signal Regions


[GeV]0M400 600 800 100012001400160018002000

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= 40 AβCMSSM tan

[GeV]0M400 600 800 100012001400160018002000

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HA

D B

ox S

igna

l Reg

ion

Eff.

(%)

02468101214161820


= 40 AβCMSSM tan



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3=7 TeVsCMS Simulation = +µ = -500 sgn

0 = 40 AβCMSSM tan

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= 40 AβCMSSM tan

MU Box Signal Regions ELE Box Signal Regions

inclusive razor analysis results – 27-10-11crogan/files/razor_27_10_11.pdf · inclusive razor...

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